Article

A study of factors affecting the utility of implicit relevance feedback

Authors:

Joemon M. JoseAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 35 - 42

https://doi.org/10.1145/1076034.1076044

Published: 15 August 2005 Publication History

Abstract

Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it performs well and when it performs poorly. In this paper we investigate how the use and effectiveness of IRF is affected by three factors: search task complexity, the search experience of the user and the stage in the search. Our findings suggest that all three of these factors contribute to the utility of IRF.

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Cited By

Tang HBenerradi JMaior HPike MLandowska AWilson M(2024)Exploring the Impact of Verbal-Imagery Cognitive Style on Web Search Behaviour and Mental WorkloadProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638313(303-316)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638313
Deckers NFröbe MKiesel JPandolfo GSchröder CStein BPotthast M(2023)The Infinite Index: Information Retrieval on Generative Text-To-Image ModelsProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578327(172-186)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578327
Huurdeman HKamps J(2020)Designing Multistage Search Systems to Support the Information Seeking ProcessUnderstanding and Improving Information Search10.1007/978-3-030-38825-6_7(113-137)Online publication date: 30-May-2020
https://doi.org/10.1007/978-3-030-38825-6_7
Show More Cited By

Index Terms

A study of factors affecting the utility of implicit relevance feedback
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

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SIGIR

SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Tang HBenerradi JMaior HPike MLandowska AWilson M(2024)Exploring the Impact of Verbal-Imagery Cognitive Style on Web Search Behaviour and Mental WorkloadProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638313(303-316)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638313
Deckers NFröbe MKiesel JPandolfo GSchröder CStein BPotthast M(2023)The Infinite Index: Information Retrieval on Generative Text-To-Image ModelsProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578327(172-186)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578327
Huurdeman HKamps J(2020)Designing Multistage Search Systems to Support the Information Seeking ProcessUnderstanding and Improving Information Search10.1007/978-3-030-38825-6_7(113-137)Online publication date: 30-May-2020
https://doi.org/10.1007/978-3-030-38825-6_7
Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Serim BJacucci GBrewster SFitzpatrick GCox AKostakos V(2019)Explicating "Implicit Interaction"Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300647(1-16)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300647
Yari Zanganeh MHariri N(2018)The role of emotional aspects in the information retrieval from the webOnline Information Review10.1108/OIR-04-2016-012142:4(520-534)Online publication date: 30-Jul-2018
https://doi.org/10.1108/OIR-04-2016-0121
Jannach DLerche LZanker M(2018)Recommending Based on Implicit FeedbackSocial Information Access10.1007/978-3-319-90092-6_14(510-569)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_14
Vechtomova O(2018)Relevance Feedback for Text RetrievalEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_949(3160-3161)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_949
Zhang PYu QHou YSong DLi JHu B(2017)A Distribution Separation Method Using Irrelevance Feedback Data for Information RetrievalACM Transactions on Intelligent Systems and Technology10.1145/29946088:3(1-26)Online publication date: 12-Jan-2017
https://dl.acm.org/doi/10.1145/2994608
Liu CWei Y(2016)The impacts of time constraint on users' search strategy during search processProceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology10.5555/3017447.3017498(1-9)Online publication date: 14-Oct-2016
https://dl.acm.org/doi/10.5555/3017447.3017498
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